
A framework to annotate sparse entities rapidly in unstructured corpora using auto-annotation. We then use flexible stopping criteria to learn sequence labeling models incrementally from annotated sentences without compromising the quality of the learned models.

A comprehensive empirical evaluation of the proposed framework by testing it on six entity classes from three public datasets.

An open source implementation of the framework within the widely used Stanford CoreNLP package [1]}.Codes and data can be found at https://github.com/halolimat/SpExtor

